This week’s lab introduced us to six data classification systems. We used four of these classification systems in ArcGIS Pro to display the distribution of senior citizens in Miami Dade County, FL. The four data classification systems we used in ArcGIS Pro were Equal Interval, Quantile, Standard Deviation, and Natural Breaks. The symbology feature in ArcGIS was used to select each different data classification. Each classification system was displayed on the map with a different color ramp to display the rank and hierarchy of each data set.
The Equal Interval classification method divides the data into equal ranges. In the map, ArcGIS displays an equal interval of 15.83 for each data set. The map reveals that the lowest percentage of seniors live in the larger census tracts on the outskirts of Miami Dade County and the population is more concentrated in the center. Some data is concealed in this map. The other maps show that the lower census tracts on the map have the lowest population. Meanwhile, on this map, the eastern-most tract shows the same population percentage as the southern-most tracts. This map does not accurately display the census tract with the lowest percentage of senior citizens.
The Quantile classification method divides the data into an equal number of observation classes. In the map, ArcGIS divided the percentage of senior citizens into 5 classes. We can see that the lowest percentage of senior citizens are in the southern-most tracts. The highest percentage of senior citizens are in the eastern center of Miami Dade County, closer to Miami. This map conceals that tract 90.40 contains the highest number of senior citizens because it groups the other tracts into the same observation class.
The Standard Deviation classification divides the data based on a curve. The data is displayed with a divergent color ramp, showing the farthest sides of the curve as darker colors and the center of the curve as the lightest colors. I don’t think this classification method is effective with this data set because we are showing data that has a ranked hierarchy, and we need to clearly show the data from least to most percentage per tract area. I think a color ramp of lightest to darkest works better for this data.
The Natural Break data classification divides the data based on an algorithm to make similarities within the data stand out. I think that this classification method displayed the data most accurately (see map image above). On the map, we can see that tract 90.40 stands out clearly against the other census tracts as having the highest population of senior citizens over the age of 65. There is also a differentiation between the southern-most tracts and the eastern-most tract. This classification shows the data as a ranked hierarchy, and I used a color ramp that displays color from light to dark to show that the lighter tracts have the lowest percentage, and the darkest tract has the highest percentage of senior citizens.
Comments
Post a Comment